Improve customer experience and reduce downtime, avoiding service disruptions, data loss, and interruption to execution
Member Rolling Upgrade
Upgrade cluster members individually in a running cluster without service interruption.
In the diagram on the left, the cluster is being incrementally upgraded via Rolling Upgrade. It currently has two members upgraded to version n+1, with the third member remaining at version n. The cluster as a whole is operating as if it has all members at version n, until all members are upgraded to version n+1.
Cross-Version Client Compatibility with Rolling Upgrade
Upgrade any of your applications independently of upgrades to your servers and other applications, instead of having to upgrade all applications together along with the server upgrades. Applications built on Hazelcast client versions that are different from the server versions (but are within the same major release number) can connect to the cluster and continue to run. This supports mixing clients of different languages and versions in one cluster.
Member Rolling Upgrade
Rolling Upgrade lets you upgrade one member at a time in a cluster, while still allowing the member to participate in the cluster after the upgrade. This lets you continue operations without having to remove members or shut down the entire cluster during an upgrade process.
Cross-Version Client Compatibility
Enriching Data Streams with Hazelcast Jet
Enrichment is a frequent technical use case in stream processing. It is a translation of the traditional star schema into the low-latency continuous processing world: the stream of facts is enriched using slowly changing dimension data.
In this webinar you will learn how to do high-performance stream enrichment. We’ll discuss multiple ways of enrichment, explaining the trade-offs. We will feature hands-on examples and live coding using Hazelcast Jet 0.7.
The Evolution of Stream Processing and Top Use Cases
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing?
In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.